The Indian journal of tuberculosis · 2025
From data to decisions: Statistical tools and Artificial Intelligence in tuberculosis Operational Research.
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Arora V K, Aggarwal Nishi, Rajpal Sanjay
Аннотация
BACKGROUND: Tuberculosis (TB) remains a major public health challenge, especially in low- and middle-income countries. Operational Research (OR), supported by robust statistical methods, plays a critical role in optimizing TB control strategies.
OBJECTIVE: This review highlights the statistical tools applied in TB Operational Research, their applications, and the emerging role of Artificial Intelligence (AI) in strengthening data-driven decision-making.
METHODS: We examine classical statistical approaches alongside predictive modeling, cost-effectiveness analysis, and AI-based frameworks. Case examples from diverse settings illustrate their practical impact.
FINDINGS: Statistical methods underpin surveillance, diagnosis, treatment evaluation, and policy modeling in TB programs. AI-driven techniques, such as machine learning and deep learning, are expanding the analytical landscape by enhancing prediction, identifying high-risk populations, and enabling real-time program monitoring.
CONCLUSION: Statistical tools from traditional inference to AI-modeling are essential for advancing TB control. Strengthening methodological rigor, reporting standards and interdisciplinary collaboration will be pivotal in harnessing data for effective TB elimination strategies.
OBJECTIVE: This review highlights the statistical tools applied in TB Operational Research, their applications, and the emerging role of Artificial Intelligence (AI) in strengthening data-driven decision-making.
METHODS: We examine classical statistical approaches alongside predictive modeling, cost-effectiveness analysis, and AI-based frameworks. Case examples from diverse settings illustrate their practical impact.
FINDINGS: Statistical methods underpin surveillance, diagnosis, treatment evaluation, and policy modeling in TB programs. AI-driven techniques, such as machine learning and deep learning, are expanding the analytical landscape by enhancing prediction, identifying high-risk populations, and enabling real-time program monitoring.
CONCLUSION: Statistical tools from traditional inference to AI-modeling are essential for advancing TB control. Strengthening methodological rigor, reporting standards and interdisciplinary collaboration will be pivotal in harnessing data for effective TB elimination strategies.